More Ethereum Locked: Bitmine Immersion Extends Its ETH Staking – Here’s How Much

bitcoinistPublished on 2026-01-15Last updated on 2026-01-15

Abstract

Following a brief rebound in Ethereum's price, institutional staking activity is increasing, with significant amounts of ETH being locked. Bitmine Immersion, led by billionaire Tom Lee, has staked an additional 154,208 ETH worth $478.77 million within a 6-hour period, demonstrating strong conviction in Ethereum's long-term prospects. This brings the company's total staked ETH to 1.344 million, valued at approximately $4.17 billion. Separately, SharpLink Gaming has generated over 500 ETH in staking rewards last week, with cumulative rewards now reaching 11,157 ETH since its ETH treasury launch. Both companies are shifting from passive holding to active network participation, contributing to Ethereum's security and decentralization while earning yields. SharpLink also deployed $170 million in ETH on Linea, integrating native yield, restaking rewards, and direct incentives through institutional-grade infrastructure.

As the price of Ethereum slowly picks up pace following a brief rebound, a significant portion of the leading altcoin is currently being locked away in staking activity. Many institutions, such as Bitmine Immersion, have ventured into ETH staking, demonstrating the growing faith and interest in the investment method.

Bitmine’s Ethereum Staking Gets A Boost

In the burgeoning cryptocurrency market, Bitmine Immersion, a leading public company, continues to make decisive steps into the growing Ethereum ecosystem. Bitmine Immersion’s step into the ecosystem is evidenced by the company’s rising participation in ETH staking.

The public firm keeps extending its staking operations and reinforcing its commitment to on-chain yield generation following its latest move. This move was reported by Lookonchain, a popular on-chain data analytics platform, in a recent post on the X platform. Furthermore, the move coincides with staking’s continued development from a specialized tactic to a fundamental element of institutional cryptocurrency involvement, providing both recurrent benefits and a closer alignment with network security.

Source: Chart from Lookonchain on X

As seen in the report, the firm, led by industry leader and billionaire Tom Lee, has staked another 154,208 ETH valued at a staggering $478.77 million. Interestingly, the massive ETH staking was carried out within a 6-hour time frame, reflecting the firm’s robust conviction in the altcoin’s long-term prospects.

After the latest staking operation, the company has now staked a total of 1.344,224 ETH worth approximately $4.17 billion. By increasing its ETH stake, Bitmine Immersion is demonstrating its interest in Ethereum, from scaling upgrades to the ongoing expansion of DeFi and tokenized assets.

SharpLink Deepens Exposure With Expanded Staking Efforts

Another company making waves in the Ethereum staking is SharpLink Gaming, a move that was initiated alongside the launch of its ETH treasury since June 2. According to a report from the firm’s official page on X, they recently generated over 500 ETH in staking rewards last week.

SharpLink ETH staking rewards underscore its expanded participation in on-chain yield and increasing interest in the altcoin and its ecosystem. This growth highlights a larger trend as more businesses are moving from passive holding to active network participation, making Ethereum staking a key component of their business strategy.

With this additional ETH, SharpLink’s total cumulative staking rewards are now sitting at 11,157 ETH since it was launched. By dedicating more of its ETH holdings to validators, the firm is indirectly contributing to Ethereum’s security and decentralization while reaping the benefits of a constant flow of rewards.

Prior to the development, SharpLink deployed $170 million in ETH with a first-of-its-kind enhanced yield on Linea. Specifically, this move integrates native ETH yield, restaking rewards from Eigencloud, and direct incentives from Linea and Etherfi within an institutional-grade qualified custodian with the help of Anchorage. SharpLink has declared this the most productive way to hold ETH with institutional-grade infrastructure.

ETH trading at $3,340 on the 1D chart | Source: ETHUSDT on Tradingview.com

Related Questions

QHow much additional ETH did Bitmine Immersion stake in its recent move, and what is its total value?

ABitmine Immersion staked an additional 154,208 ETH, valued at approximately $478.77 million.

QWhat is the total amount of ETH that Bitmine Immersion has staked so far, and what is its estimated worth?

ABitmine Immersion has staked a total of 1,344,224 ETH, worth approximately $4.17 billion.

QWhich on-chain analytics platform reported Bitmine Immersion's latest ETH staking activity?

ALookonchain, a popular on-chain data analytics platform, reported Bitmine Immersion's latest ETH staking activity.

QHow much ETH in staking rewards did SharpLink Gaming generate last week?

ASharpLink Gaming generated over 500 ETH in staking rewards last week.

QWhat is the total cumulative staking rewards that SharpLink has earned since it launched its ETH treasury?

ASharpLink's total cumulative staking rewards are 11,157 ETH since its launch.

Related Reads

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbit1h ago

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbit1h ago

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit1h ago

Token Inefficient, Economy Tokenless

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

In 2026, a historic shift occurred in AI as major cloud providers' inference spending surpassed training spending for the first time, signaling a move from "building large models" to "using large models." This shifts the core challenge from computing power to the "memory wall"—the bottleneck of data movement (model weights, activations, KV Cache) between external DRAM and processors, where energy and latency from data transfer far exceed computation itself. Companies like Nvidia face GPU idle time due to bandwidth limits. In contrast, Cerebras Systems adopts a radical "wafer-scale" approach with its Wafer-Scale Engine (WSE). Instead of cutting a silicon wafer into many chips, Cerebras uses almost the entire wafer as one massive chip (WSE-3). This design provides 44GB of on-chip SRAM, delivering memory bandwidth thousands of times higher than traditional HBM (e.g., 21 PB/s vs. Nvidia B200). For LLM inference, weights are streamed layer-by-layer from external MemoryX storage to the chip, avoiding HBM bottlenecks. This results in token generation speeds 1.5–5 times faster than Nvidia's B200 in some models and significant advantages in first-token latency and long-context tasks. Additionally, Cerebras's architecture offers much lower interconnect power consumption (0.15 pJ/bit vs. GPU's ~10 pJ/bit). However, Cerebras faces challenges: SRAM scaling has slowed with advanced nodes, limiting future capacity gains; the chip requires specialized liquid cooling and custom software stacks; and its external I/O bandwidth (150 GB/s) is low compared to NVLink, hindering multi-system scaling for very large models. Competition is intensifying. Major players are pursuing three paths: 1) Developing proprietary inference ASICs (e.g., Google TPU, Microsoft Maia), 2) Leveraging advanced packaging (e.g., TSMC's SoW) to democratize wafer-scale-like integration, potentially eroding Cerebras's process advantage within a few years, and 3) Exploring optical interconnects for ultimate bandwidth. Commercially, Cerebras is transitioning from a hardware vendor to a service provider, facing the immense challenge of building high-power, specialized data centers to meet large contracts (e.g., 250MW/year from 2026–2028). In conclusion, the AI inference era presents a fundamental architectural trade-off. Cerebras opts for extreme physical optimization for low-latency, single-task performance, while Nvidia prioritizes versatility and massive cluster throughput. The path forward remains uncertain, with technology and business models still evolving in the race toward advanced AI.

marsbit1h ago

Crossing the 'Memory Wall': The Wafer-Level Revolution and Computing Power Routes in the AI Inference Era

marsbit1h ago

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

**Title: Has Bitcoin's Rebound Ended, Entering the Late Bear Market Phase?** **Summary:** Bitcoin's price has declined by 13% this week, signaling a potential return to late-stage bear market conditions. The price fell to around $67k, positioned between the Realized Price and Realized Cap Weighted Average. For the first time since early 2022, the Short-Term Holder cost basis has dropped below this key average, confirming a hallmark of late-cycle bear markets. Profitability metrics have collapsed sharply. The 7-day average of the Realized Profit/Loss ratio plummeted from a local high of 3.16 to 0.29, mirroring the February panic sell-off. Critically, the 90-day average never breached the threshold of 2, indicating the recent rally to $82k was a bear market bounce, not a structural shift. Realized losses surged to $1.35 billion daily, with $770 million coming from Long-Term Holders selling at a loss. This accelerating redistribution of supply from weak to strong hands is a necessary but ongoing process for a market bottom. The rally stalled almost precisely at the aggregate cost basis (~$83k) of US spot Bitcoin ETF investors, turning that level into strong resistance and leaving the average ETF holder underwater again. Spot market flows have turned decisively negative, showing sellers are dominating order books despite the price drop. While a significant futures long liquidation event cleared over $400 million in leverage, providing a potential reset, sustained spot demand is yet to materialize. Options markets continue to price in higher future volatility (Implied Volatility) than recent price action (Realized Volatility) has shown, with a persistent skew towards put options, indicating ongoing demand for downside protection. In conclusion, multiple metrics point to a fragile market structure. Resistance at the ETF cost basis, accelerating realized losses, dominant spot selling, and cautious options pricing all suggest the bear market trend persists. A sustainable recovery likely requires a resurgence of spot demand, ETF holders returning to profit, and a clear reduction in selling pressure.

marsbit1h ago

Has Bitcoin's 'Rebound Ended', Officially Entering the Late Bear Market Phase?

marsbit1h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片